Overview

Dataset statistics

Number of variables16
Number of observations48895
Missing cells20141
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 MiB
Average record size in memory128.0 B

Variable types

Numeric10
Text3
Categorical2
DateTime1

Alerts

host_id is highly overall correlated with idHigh correlation
id is highly overall correlated with host_idHigh correlation
latitude is highly overall correlated with neighbourhood_groupHigh correlation
longitude is highly overall correlated with neighbourhood_groupHigh correlation
neighbourhood_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
last_review has 10052 (20.6%) missing valuesMissing
reviews_per_month has 10052 (20.6%) missing valuesMissing
minimum_nights is highly skewed (γ1 = 21.82727453)Skewed
id has unique valuesUnique
number_of_reviews has 10052 (20.6%) zerosZeros
availability_365 has 17533 (35.9%) zerosZeros

Reproduction

Analysis started2024-06-19 18:59:47.967505
Analysis finished2024-06-19 19:00:24.937694
Duration36.97 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48895
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19017143
Minimum2539
Maximum36487245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2024-06-19T19:00:25.174338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2539
5-th percentile1222382.7
Q19471945
median19677284
Q329152178
95-th percentile35259101
Maximum36487245
Range36484706
Interquartile range (IQR)19680234

Descriptive statistics

Standard deviation10983108
Coefficient of variation (CV)0.57753724
Kurtosis-1.2277483
Mean19017143
Median Absolute Deviation (MAD)9908242
Skewness-0.090257375
Sum9.2984322 × 1011
Variance1.2062867 × 1014
MonotonicityStrictly increasing
2024-06-19T19:00:25.889228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2539 1
 
< 0.1%
25583366 1
 
< 0.1%
25551687 1
 
< 0.1%
25552076 1
 
< 0.1%
25554120 1
 
< 0.1%
25568873 1
 
< 0.1%
25571627 1
 
< 0.1%
25572892 1
 
< 0.1%
25580113 1
 
< 0.1%
25580283 1
 
< 0.1%
Other values (48885) 48885
> 99.9%
ValueCountFrequency (%)
2539 1
< 0.1%
2595 1
< 0.1%
3647 1
< 0.1%
3831 1
< 0.1%
5022 1
< 0.1%
5099 1
< 0.1%
5121 1
< 0.1%
5178 1
< 0.1%
5203 1
< 0.1%
5238 1
< 0.1%
ValueCountFrequency (%)
36487245 1
< 0.1%
36485609 1
< 0.1%
36485431 1
< 0.1%
36485057 1
< 0.1%
36484665 1
< 0.1%
36484363 1
< 0.1%
36484087 1
< 0.1%
36483152 1
< 0.1%
36483010 1
< 0.1%
36482809 1
< 0.1%

name
Text

Distinct47905
Distinct (%)98.0%
Missing16
Missing (%)< 0.1%
Memory size382.1 KiB
2024-06-19T19:00:26.375202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length179
Median length78
Mean length36.911148
Min length1

Characters and Unicode

Total characters1804180
Distinct characters776
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47260 ?
Unique (%)96.7%

Sample

1st rowClean & quiet apt home by the park
2nd rowSkylit Midtown Castle
3rd rowTHE VILLAGE OF HARLEM....NEW YORK !
4th rowCozy Entire Floor of Brownstone
5th rowEntire Apt: Spacious Studio/Loft by central park
ValueCountFrequency (%)
in 16752
 
5.6%
room 10038
 
3.4%
8430
 
2.8%
bedroom 7601
 
2.5%
private 7158
 
2.4%
apartment 6695
 
2.2%
cozy 4991
 
1.7%
apt 4618
 
1.5%
brooklyn 4049
 
1.4%
studio 3988
 
1.3%
Other values (12552) 224301
75.1%
2024-06-19T19:00:27.182128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
251424
 
13.9%
e 124635
 
6.9%
o 122324
 
6.8%
t 105261
 
5.8%
a 103586
 
5.7%
r 97946
 
5.4%
i 94651
 
5.2%
n 94611
 
5.2%
l 51723
 
2.9%
m 49121
 
2.7%
Other values (766) 708898
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1804180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
251424
 
13.9%
e 124635
 
6.9%
o 122324
 
6.8%
t 105261
 
5.8%
a 103586
 
5.7%
r 97946
 
5.4%
i 94651
 
5.2%
n 94611
 
5.2%
l 51723
 
2.9%
m 49121
 
2.7%
Other values (766) 708898
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1804180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
251424
 
13.9%
e 124635
 
6.9%
o 122324
 
6.8%
t 105261
 
5.8%
a 103586
 
5.7%
r 97946
 
5.4%
i 94651
 
5.2%
n 94611
 
5.2%
l 51723
 
2.9%
m 49121
 
2.7%
Other values (766) 708898
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1804180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
251424
 
13.9%
e 124635
 
6.9%
o 122324
 
6.8%
t 105261
 
5.8%
a 103586
 
5.7%
r 97946
 
5.4%
i 94651
 
5.2%
n 94611
 
5.2%
l 51723
 
2.9%
m 49121
 
2.7%
Other values (766) 708898
39.3%

host_id
Real number (ℝ)

HIGH CORRELATION 

Distinct37457
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67620011
Minimum2438
Maximum2.7432131 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2024-06-19T19:00:27.508218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2438
5-th percentile815564.1
Q17822033
median30793816
Q31.0743442 × 108
95-th percentile2.417646 × 108
Maximum2.7432131 × 108
Range2.7431888 × 108
Interquartile range (IQR)99612390

Descriptive statistics

Standard deviation78610967
Coefficient of variation (CV)1.16254
Kurtosis0.16910576
Mean67620011
Median Absolute Deviation (MAD)27543913
Skewness1.2062139
Sum3.3062804 × 1012
Variance6.1796841 × 1015
MonotonicityNot monotonic
2024-06-19T19:00:27.784138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219517861 327
 
0.7%
107434423 232
 
0.5%
30283594 121
 
0.2%
137358866 103
 
0.2%
16098958 96
 
0.2%
12243051 96
 
0.2%
61391963 91
 
0.2%
22541573 87
 
0.2%
200380610 65
 
0.1%
7503643 52
 
0.1%
Other values (37447) 47625
97.4%
ValueCountFrequency (%)
2438 1
 
< 0.1%
2571 1
 
< 0.1%
2787 6
< 0.1%
2845 2
 
< 0.1%
2868 1
 
< 0.1%
2881 2
 
< 0.1%
3151 1
 
< 0.1%
3211 1
 
< 0.1%
3415 1
 
< 0.1%
3563 1
 
< 0.1%
ValueCountFrequency (%)
274321313 1
< 0.1%
274311461 1
< 0.1%
274307600 1
< 0.1%
274298453 1
< 0.1%
274273284 1
< 0.1%
274225617 1
< 0.1%
274195458 1
< 0.1%
274188386 1
< 0.1%
274103383 1
< 0.1%
274079964 1
< 0.1%
Distinct11452
Distinct (%)23.4%
Missing21
Missing (%)< 0.1%
Memory size382.1 KiB
2024-06-19T19:00:28.283898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length31
Mean length6.1248721
Min length1

Characters and Unicode

Total characters299347
Distinct characters204
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6903 ?
Unique (%)14.1%

Sample

1st rowJohn
2nd rowJennifer
3rd rowElisabeth
4th rowLisaRoxanne
5th rowLaura
ValueCountFrequency (%)
1120
 
2.1%
and 625
 
1.1%
michael 460
 
0.8%
david 449
 
0.8%
sonder 423
 
0.8%
nyc 338
 
0.6%
john 337
 
0.6%
alex 330
 
0.6%
laura 293
 
0.5%
maria 244
 
0.4%
Other values (10259) 49968
91.5%
2024-06-19T19:00:29.127809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 37929
 
12.7%
e 28680
 
9.6%
i 24284
 
8.1%
n 24092
 
8.0%
r 17861
 
6.0%
l 15327
 
5.1%
o 12743
 
4.3%
t 9401
 
3.1%
s 9147
 
3.1%
h 9040
 
3.0%
Other values (194) 110843
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 299347
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 37929
 
12.7%
e 28680
 
9.6%
i 24284
 
8.1%
n 24092
 
8.0%
r 17861
 
6.0%
l 15327
 
5.1%
o 12743
 
4.3%
t 9401
 
3.1%
s 9147
 
3.1%
h 9040
 
3.0%
Other values (194) 110843
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 299347
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 37929
 
12.7%
e 28680
 
9.6%
i 24284
 
8.1%
n 24092
 
8.0%
r 17861
 
6.0%
l 15327
 
5.1%
o 12743
 
4.3%
t 9401
 
3.1%
s 9147
 
3.1%
h 9040
 
3.0%
Other values (194) 110843
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 299347
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 37929
 
12.7%
e 28680
 
9.6%
i 24284
 
8.1%
n 24092
 
8.0%
r 17861
 
6.0%
l 15327
 
5.1%
o 12743
 
4.3%
t 9401
 
3.1%
s 9147
 
3.1%
h 9040
 
3.0%
Other values (194) 110843
37.0%

neighbourhood_group
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size382.1 KiB
Manhattan
21661 
Brooklyn
20104 
Queens
5666 
Bronx
 
1091
Staten Island
 
373

Length

Max length13
Median length9
Mean length8.1824522
Min length5

Characters and Unicode

Total characters400081
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrooklyn
2nd rowManhattan
3rd rowManhattan
4th rowBrooklyn
5th rowManhattan

Common Values

ValueCountFrequency (%)
Manhattan 21661
44.3%
Brooklyn 20104
41.1%
Queens 5666
 
11.6%
Bronx 1091
 
2.2%
Staten Island 373
 
0.8%

Length

2024-06-19T19:00:29.452465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-19T19:00:29.728778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 21661
44.0%
brooklyn 20104
40.8%
queens 5666
 
11.5%
bronx 1091
 
2.2%
staten 373
 
0.8%
island 373
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n 70929
17.7%
a 65729
16.4%
t 44068
11.0%
o 41299
10.3%
M 21661
 
5.4%
h 21661
 
5.4%
B 21195
 
5.3%
r 21195
 
5.3%
l 20477
 
5.1%
y 20104
 
5.0%
Other values (10) 51763
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 400081
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 70929
17.7%
a 65729
16.4%
t 44068
11.0%
o 41299
10.3%
M 21661
 
5.4%
h 21661
 
5.4%
B 21195
 
5.3%
r 21195
 
5.3%
l 20477
 
5.1%
y 20104
 
5.0%
Other values (10) 51763
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 400081
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 70929
17.7%
a 65729
16.4%
t 44068
11.0%
o 41299
10.3%
M 21661
 
5.4%
h 21661
 
5.4%
B 21195
 
5.3%
r 21195
 
5.3%
l 20477
 
5.1%
y 20104
 
5.0%
Other values (10) 51763
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 400081
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 70929
17.7%
a 65729
16.4%
t 44068
11.0%
o 41299
10.3%
M 21661
 
5.4%
h 21661
 
5.4%
B 21195
 
5.3%
r 21195
 
5.3%
l 20477
 
5.1%
y 20104
 
5.0%
Other values (10) 51763
12.9%
Distinct221
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size382.1 KiB
2024-06-19T19:00:30.192024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length17
Mean length11.894795
Min length4

Characters and Unicode

Total characters581596
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowKensington
2nd rowMidtown
3rd rowHarlem
4th rowClinton Hill
5th rowEast Harlem
ValueCountFrequency (%)
east 6592
 
8.3%
side 4680
 
5.9%
williamsburg 3920
 
5.0%
harlem 3775
 
4.8%
upper 3769
 
4.8%
bedford-stuyvesant 3714
 
4.7%
heights 3586
 
4.5%
village 3164
 
4.0%
west 2759
 
3.5%
bushwick 2465
 
3.1%
Other values (233) 40681
51.4%
2024-06-19T19:00:30.970315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 53470
 
9.2%
i 42282
 
7.3%
s 39625
 
6.8%
t 38587
 
6.6%
a 37608
 
6.5%
l 34448
 
5.9%
r 33667
 
5.8%
30210
 
5.2%
n 26099
 
4.5%
o 24032
 
4.1%
Other values (44) 221568
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581596
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 53470
 
9.2%
i 42282
 
7.3%
s 39625
 
6.8%
t 38587
 
6.6%
a 37608
 
6.5%
l 34448
 
5.9%
r 33667
 
5.8%
30210
 
5.2%
n 26099
 
4.5%
o 24032
 
4.1%
Other values (44) 221568
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581596
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 53470
 
9.2%
i 42282
 
7.3%
s 39625
 
6.8%
t 38587
 
6.6%
a 37608
 
6.5%
l 34448
 
5.9%
r 33667
 
5.8%
30210
 
5.2%
n 26099
 
4.5%
o 24032
 
4.1%
Other values (44) 221568
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581596
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 53470
 
9.2%
i 42282
 
7.3%
s 39625
 
6.8%
t 38587
 
6.6%
a 37608
 
6.5%
l 34448
 
5.9%
r 33667
 
5.8%
30210
 
5.2%
n 26099
 
4.5%
o 24032
 
4.1%
Other values (44) 221568
38.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct19048
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.728949
Minimum40.49979
Maximum40.91306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2024-06-19T19:00:31.283504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40.49979
5-th percentile40.646114
Q140.6901
median40.72307
Q340.763115
95-th percentile40.825643
Maximum40.91306
Range0.41327
Interquartile range (IQR)0.073015

Descriptive statistics

Standard deviation0.054530078
Coefficient of variation (CV)0.0013388531
Kurtosis0.14884466
Mean40.728949
Median Absolute Deviation (MAD)0.03642
Skewness0.23716656
Sum1991442
Variance0.0029735294
MonotonicityNot monotonic
2024-06-19T19:00:31.572888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71813 18
 
< 0.1%
40.68444 13
 
< 0.1%
40.69414 13
 
< 0.1%
40.68634 13
 
< 0.1%
40.76125 12
 
< 0.1%
40.68537 12
 
< 0.1%
40.71171 12
 
< 0.1%
40.71353 12
 
< 0.1%
40.76189 12
 
< 0.1%
40.68683 11
 
< 0.1%
Other values (19038) 48767
99.7%
ValueCountFrequency (%)
40.49979 1
< 0.1%
40.50641 1
< 0.1%
40.50708 1
< 0.1%
40.50868 1
< 0.1%
40.50873 1
< 0.1%
40.50943 1
< 0.1%
40.51133 1
< 0.1%
40.52211 1
< 0.1%
40.52293 1
< 0.1%
40.527 1
< 0.1%
ValueCountFrequency (%)
40.91306 1
< 0.1%
40.91234 1
< 0.1%
40.91169 1
< 0.1%
40.91167 1
< 0.1%
40.90804 1
< 0.1%
40.90734 1
< 0.1%
40.90527 1
< 0.1%
40.90484 1
< 0.1%
40.90406 1
< 0.1%
40.90391 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct14718
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.95217
Minimum-74.24442
Maximum-73.71299
Zeros0
Zeros (%)0.0%
Negative48895
Negative (%)100.0%
Memory size382.1 KiB
2024-06-19T19:00:31.858787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-74.24442
5-th percentile-74.00388
Q1-73.98307
median-73.95568
Q3-73.936275
95-th percentile-73.865771
Maximum-73.71299
Range0.53143
Interquartile range (IQR)0.046795

Descriptive statistics

Standard deviation0.046156736
Coefficient of variation (CV)-0.0006241431
Kurtosis5.0216461
Mean-73.95217
Median Absolute Deviation (MAD)0.02485
Skewness1.2842102
Sum-3615891.3
Variance0.0021304443
MonotonicityNot monotonic
2024-06-19T19:00:32.162226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.95677 18
 
< 0.1%
-73.95427 18
 
< 0.1%
-73.95405 17
 
< 0.1%
-73.9506 16
 
< 0.1%
-73.94791 16
 
< 0.1%
-73.95332 16
 
< 0.1%
-73.95136 16
 
< 0.1%
-73.95669 15
 
< 0.1%
-73.95742 15
 
< 0.1%
-73.94537 15
 
< 0.1%
Other values (14708) 48733
99.7%
ValueCountFrequency (%)
-74.24442 1
< 0.1%
-74.24285 1
< 0.1%
-74.24084 1
< 0.1%
-74.23986 1
< 0.1%
-74.23914 1
< 0.1%
-74.23803 1
< 0.1%
-74.23059 1
< 0.1%
-74.21238 1
< 0.1%
-74.21017 1
< 0.1%
-74.20941 1
< 0.1%
ValueCountFrequency (%)
-73.71299 1
< 0.1%
-73.7169 1
< 0.1%
-73.71795 1
< 0.1%
-73.71829 1
< 0.1%
-73.71928 1
< 0.1%
-73.72173 1
< 0.1%
-73.72179 1
< 0.1%
-73.72247 1
< 0.1%
-73.72435 1
< 0.1%
-73.72581 1
< 0.1%

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size382.1 KiB
Entire home/apt
25409 
Private room
22326 
Shared room
 
1160

Length

Max length15
Median length15
Mean length13.535269
Min length11

Characters and Unicode

Total characters661807
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowPrivate room
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 25409
52.0%
Private room 22326
45.7%
Shared room 1160
 
2.4%

Length

2024-06-19T19:00:32.715742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-19T19:00:32.994775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
entire 25409
26.0%
home/apt 25409
26.0%
room 23486
24.0%
private 22326
22.8%
shared 1160
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 74304
11.2%
t 73144
11.1%
o 72381
10.9%
r 72381
10.9%
a 48895
 
7.4%
48895
 
7.4%
m 48895
 
7.4%
i 47735
 
7.2%
h 26569
 
4.0%
p 25409
 
3.8%
Other values (7) 123199
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 661807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 74304
11.2%
t 73144
11.1%
o 72381
10.9%
r 72381
10.9%
a 48895
 
7.4%
48895
 
7.4%
m 48895
 
7.4%
i 47735
 
7.2%
h 26569
 
4.0%
p 25409
 
3.8%
Other values (7) 123199
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 661807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 74304
11.2%
t 73144
11.1%
o 72381
10.9%
r 72381
10.9%
a 48895
 
7.4%
48895
 
7.4%
m 48895
 
7.4%
i 47735
 
7.2%
h 26569
 
4.0%
p 25409
 
3.8%
Other values (7) 123199
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 661807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 74304
11.2%
t 73144
11.1%
o 72381
10.9%
r 72381
10.9%
a 48895
 
7.4%
48895
 
7.4%
m 48895
 
7.4%
i 47735
 
7.2%
h 26569
 
4.0%
p 25409
 
3.8%
Other values (7) 123199
18.6%

price
Real number (ℝ)

Distinct674
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.72069
Minimum0
Maximum10000
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2024-06-19T19:00:33.225913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40
Q169
median106
Q3175
95-th percentile355
Maximum10000
Range10000
Interquartile range (IQR)106

Descriptive statistics

Standard deviation240.15417
Coefficient of variation (CV)1.5725058
Kurtosis585.67288
Mean152.72069
Median Absolute Deviation (MAD)46
Skewness19.118939
Sum7467278
Variance57674.025
MonotonicityNot monotonic
2024-06-19T19:00:33.760172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 2051
 
4.2%
150 2047
 
4.2%
50 1534
 
3.1%
60 1458
 
3.0%
200 1401
 
2.9%
75 1370
 
2.8%
80 1272
 
2.6%
65 1190
 
2.4%
70 1170
 
2.4%
120 1130
 
2.3%
Other values (664) 34272
70.1%
ValueCountFrequency (%)
0 11
 
< 0.1%
10 17
< 0.1%
11 3
 
< 0.1%
12 4
 
< 0.1%
13 1
 
< 0.1%
15 6
 
< 0.1%
16 6
 
< 0.1%
18 2
 
< 0.1%
19 4
 
< 0.1%
20 33
0.1%
ValueCountFrequency (%)
10000 3
< 0.1%
9999 3
< 0.1%
8500 1
 
< 0.1%
8000 1
 
< 0.1%
7703 1
 
< 0.1%
7500 2
< 0.1%
6800 1
 
< 0.1%
6500 3
< 0.1%
6419 1
 
< 0.1%
6000 2
< 0.1%

minimum_nights
Real number (ℝ)

SKEWED 

Distinct109
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0299622
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2024-06-19T19:00:34.032754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile30
Maximum1250
Range1249
Interquartile range (IQR)4

Descriptive statistics

Standard deviation20.51055
Coefficient of variation (CV)2.9175903
Kurtosis854.07166
Mean7.0299622
Median Absolute Deviation (MAD)2
Skewness21.827275
Sum343730
Variance420.68264
MonotonicityNot monotonic
2024-06-19T19:00:34.589443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12720
26.0%
2 11696
23.9%
3 7999
16.4%
30 3760
 
7.7%
4 3303
 
6.8%
5 3034
 
6.2%
7 2058
 
4.2%
6 752
 
1.5%
14 562
 
1.1%
10 483
 
1.0%
Other values (99) 2528
 
5.2%
ValueCountFrequency (%)
1 12720
26.0%
2 11696
23.9%
3 7999
16.4%
4 3303
 
6.8%
5 3034
 
6.2%
6 752
 
1.5%
7 2058
 
4.2%
8 130
 
0.3%
9 80
 
0.2%
10 483
 
1.0%
ValueCountFrequency (%)
1250 1
 
< 0.1%
1000 1
 
< 0.1%
999 3
 
< 0.1%
500 5
 
< 0.1%
480 1
 
< 0.1%
400 1
 
< 0.1%
370 1
 
< 0.1%
366 1
 
< 0.1%
365 29
0.1%
364 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct394
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.274466
Minimum0
Maximum629
Zeros10052
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2024-06-19T19:00:34.874399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q324
95-th percentile114
Maximum629
Range629
Interquartile range (IQR)23

Descriptive statistics

Standard deviation44.550582
Coefficient of variation (CV)1.9141398
Kurtosis19.529788
Mean23.274466
Median Absolute Deviation (MAD)5
Skewness3.6906346
Sum1138005
Variance1984.7544
MonotonicityNot monotonic
2024-06-19T19:00:35.155789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10052
20.6%
1 5244
 
10.7%
2 3465
 
7.1%
3 2520
 
5.2%
4 1994
 
4.1%
5 1618
 
3.3%
6 1357
 
2.8%
7 1179
 
2.4%
8 1127
 
2.3%
9 964
 
2.0%
Other values (384) 19375
39.6%
ValueCountFrequency (%)
0 10052
20.6%
1 5244
10.7%
2 3465
 
7.1%
3 2520
 
5.2%
4 1994
 
4.1%
5 1618
 
3.3%
6 1357
 
2.8%
7 1179
 
2.4%
8 1127
 
2.3%
9 964
 
2.0%
ValueCountFrequency (%)
629 1
< 0.1%
607 1
< 0.1%
597 1
< 0.1%
594 1
< 0.1%
576 1
< 0.1%
543 1
< 0.1%
540 1
< 0.1%
510 1
< 0.1%
488 1
< 0.1%
480 1
< 0.1%

last_review
Date

MISSING 

Distinct1764
Distinct (%)4.5%
Missing10052
Missing (%)20.6%
Memory size382.1 KiB
Minimum2011-03-28 00:00:00
Maximum2019-07-08 00:00:00
2024-06-19T19:00:35.570720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:36.019045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct937
Distinct (%)2.4%
Missing10052
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean1.3732214
Minimum0.01
Maximum58.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2024-06-19T19:00:36.432766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.19
median0.72
Q32.02
95-th percentile4.64
Maximum58.5
Range58.49
Interquartile range (IQR)1.83

Descriptive statistics

Standard deviation1.680442
Coefficient of variation (CV)1.2237225
Kurtosis42.493469
Mean1.3732214
Median Absolute Deviation (MAD)0.62
Skewness3.1301885
Sum53340.04
Variance2.8238853
MonotonicityNot monotonic
2024-06-19T19:00:36.830389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 919
 
1.9%
1 893
 
1.8%
0.05 893
 
1.8%
0.03 804
 
1.6%
0.16 667
 
1.4%
0.04 655
 
1.3%
0.08 596
 
1.2%
0.09 593
 
1.2%
0.06 579
 
1.2%
0.11 539
 
1.1%
Other values (927) 31705
64.8%
(Missing) 10052
 
20.6%
ValueCountFrequency (%)
0.01 42
 
0.1%
0.02 919
1.9%
0.03 804
1.6%
0.04 655
1.3%
0.05 893
1.8%
0.06 579
1.2%
0.07 466
1.0%
0.08 596
1.2%
0.09 593
1.2%
0.1 457
0.9%
ValueCountFrequency (%)
58.5 1
< 0.1%
27.95 1
< 0.1%
20.94 1
< 0.1%
19.75 1
< 0.1%
17.82 1
< 0.1%
16.81 1
< 0.1%
16.22 1
< 0.1%
16.03 1
< 0.1%
15.78 1
< 0.1%
15.32 1
< 0.1%
Distinct47
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.143982
Minimum1
Maximum327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2024-06-19T19:00:37.218743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile15
Maximum327
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation32.952519
Coefficient of variation (CV)4.6126262
Kurtosis67.550888
Mean7.143982
Median Absolute Deviation (MAD)0
Skewness7.9331739
Sum349305
Variance1085.8685
MonotonicityNot monotonic
2024-06-19T19:00:37.576840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 32303
66.1%
2 6658
 
13.6%
3 2853
 
5.8%
4 1440
 
2.9%
5 845
 
1.7%
6 570
 
1.2%
8 416
 
0.9%
7 399
 
0.8%
327 327
 
0.7%
9 234
 
0.5%
Other values (37) 2850
 
5.8%
ValueCountFrequency (%)
1 32303
66.1%
2 6658
 
13.6%
3 2853
 
5.8%
4 1440
 
2.9%
5 845
 
1.7%
6 570
 
1.2%
7 399
 
0.8%
8 416
 
0.9%
9 234
 
0.5%
10 210
 
0.4%
ValueCountFrequency (%)
327 327
0.7%
232 232
0.5%
121 121
 
0.2%
103 103
 
0.2%
96 192
0.4%
91 91
 
0.2%
87 87
 
0.2%
65 65
 
0.1%
52 104
 
0.2%
50 50
 
0.1%

availability_365
Real number (ℝ)

ZEROS 

Distinct366
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.78133
Minimum0
Maximum365
Zeros17533
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size382.1 KiB
2024-06-19T19:00:38.080123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median45
Q3227
95-th percentile359
Maximum365
Range365
Interquartile range (IQR)227

Descriptive statistics

Standard deviation131.62229
Coefficient of variation (CV)1.1670575
Kurtosis-0.99753405
Mean112.78133
Median Absolute Deviation (MAD)45
Skewness0.76340758
Sum5514443
Variance17324.427
MonotonicityNot monotonic
2024-06-19T19:00:38.578624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17533
35.9%
365 1295
 
2.6%
364 491
 
1.0%
1 408
 
0.8%
89 361
 
0.7%
5 340
 
0.7%
3 306
 
0.6%
179 301
 
0.6%
90 290
 
0.6%
2 270
 
0.6%
Other values (356) 27300
55.8%
ValueCountFrequency (%)
0 17533
35.9%
1 408
 
0.8%
2 270
 
0.6%
3 306
 
0.6%
4 233
 
0.5%
5 340
 
0.7%
6 245
 
0.5%
7 219
 
0.4%
8 233
 
0.5%
9 193
 
0.4%
ValueCountFrequency (%)
365 1295
2.6%
364 491
 
1.0%
363 239
 
0.5%
362 166
 
0.3%
361 111
 
0.2%
360 102
 
0.2%
359 135
 
0.3%
358 180
 
0.4%
357 95
 
0.2%
356 78
 
0.2%

Interactions

2024-06-19T19:00:19.372119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:51.956992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:57.083372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:59.634990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:02.184886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:04.981750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:07.704188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:11.244659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:14.136613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:16.762562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:19.650130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:52.247024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:57.347609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:59.879418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:02.456192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:05.235565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:08.092039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:11.509964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:14.392412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:17.030563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:19.907666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:52.610228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:57.616651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:00.124157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:02.744291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:05.499052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:08.441891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:11.777251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:14.649685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:17.269402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:20.180020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:52.966831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:57.857527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:00.368573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:02.978080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:05.734059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:08.819200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:12.038717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:14.930259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:17.510391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:20.434120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:53.299438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:58.112235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:00.626616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:03.439843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:05.986557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:09.161724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:12.273431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:15.189700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:17.773216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:20.680199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:53.763598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:58.371877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:00.862130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:03.702001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:06.217276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:09.549418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:12.547657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:15.434070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:18.048996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:20.987058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:55.071847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:58.642499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:01.114501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:03.948848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:06.469286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:09.877160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:12.831383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:15.739352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:18.295894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:21.319163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:55.892027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:58.878846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:01.385068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:04.197698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:06.727791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:10.236839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:13.352242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:15.999963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:18.579988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:21.724740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:56.427848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:59.119921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:01.670835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:04.464060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:06.998622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:10.668757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:13.617136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:16.245496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:18.846022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:22.124079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:56.799017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T18:59:59.379247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:01.934538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:04.727160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:07.295689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:10.989418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:13.885215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:16.499144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:00:19.113805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-06-19T19:00:39.012969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
availability_365calculated_host_listings_counthost_ididlatitudelongitudeminimum_nightsneighbourhood_groupnumber_of_reviewspricereviews_per_monthroom_type
availability_3651.0000.4070.1730.166-0.0070.0690.0760.0830.2370.0860.3920.087
calculated_host_listings_count0.4071.0000.1470.1350.0040.0640.0640.0890.056-0.1060.1460.097
host_id0.1730.1471.0000.5590.0500.109-0.1300.100-0.128-0.0720.2680.092
id0.1660.1350.5591.0000.0050.071-0.0580.064-0.308-0.0210.3600.070
latitude-0.0070.0040.0500.0051.0000.0350.0220.539-0.0440.136-0.0230.117
longitude0.0690.0640.1090.0710.0351.000-0.1190.6540.080-0.4380.1190.158
minimum_nights0.0760.064-0.130-0.0580.022-0.1191.0000.003-0.1750.101-0.2890.012
neighbourhood_group0.0830.0890.1000.0640.5390.6540.0031.000-0.0210.1250.0480.126
number_of_reviews0.2370.056-0.128-0.308-0.0440.080-0.175-0.0211.000-0.0550.7060.021
price0.086-0.106-0.072-0.0210.136-0.4380.1010.125-0.0551.000-0.0190.025
reviews_per_month0.3920.1460.2680.360-0.0230.119-0.2890.0480.706-0.0191.0000.029
room_type0.0870.0970.0920.0700.1170.1580.0120.1260.0210.0250.0291.000

Missing values

2024-06-19T19:00:22.743871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-19T19:00:23.715343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-19T19:00:24.566572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
02539Clean & quiet apt home by the park2787JohnBrooklynKensington40.64749-73.97237Private room149192018-10-190.216365
12595Skylit Midtown Castle2845JenniferManhattanMidtown40.75362-73.98377Entire home/apt2251452019-05-210.382355
23647THE VILLAGE OF HARLEM....NEW YORK !4632ElisabethManhattanHarlem40.80902-73.94190Private room15030NaNNaN1365
33831Cozy Entire Floor of Brownstone4869LisaRoxanneBrooklynClinton Hill40.68514-73.95976Entire home/apt8912702019-07-054.641194
45022Entire Apt: Spacious Studio/Loft by central park7192LauraManhattanEast Harlem40.79851-73.94399Entire home/apt801092018-11-190.1010
55099Large Cozy 1 BR Apartment In Midtown East7322ChrisManhattanMurray Hill40.74767-73.97500Entire home/apt2003742019-06-220.591129
65121BlissArtsSpace!7356GaronBrooklynBedford-Stuyvesant40.68688-73.95596Private room6045492017-10-050.4010
75178Large Furnished Room Near B'way8967ShunichiManhattanHell's Kitchen40.76489-73.98493Private room7924302019-06-243.471220
85203Cozy Clean Guest Room - Family Apt7490MaryEllenManhattanUpper West Side40.80178-73.96723Private room7921182017-07-210.9910
95238Cute & Cozy Lower East Side 1 bdrm7549BenManhattanChinatown40.71344-73.99037Entire home/apt15011602019-06-091.334188
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
4888536482809Stunning Bedroom NYC! Walking to Central Park!!131529729KendallManhattanEast Harlem40.79633-73.93605Private room7520NaNNaN2353
4888636483010Comfy 1 Bedroom in Midtown East274311461ScottManhattanMidtown40.75561-73.96723Entire home/apt20060NaNNaN1176
4888736483152Garden Jewel Apartment in Williamsburg New York208514239MelkiBrooklynWilliamsburg40.71232-73.94220Entire home/apt17010NaNNaN3365
4888836484087Spacious Room w/ Private Rooftop, Central location274321313KatManhattanHell's Kitchen40.76392-73.99183Private room12540NaNNaN131
4888936484363QUIT PRIVATE HOUSE107716952MichaelQueensJamaica40.69137-73.80844Private room6510NaNNaN2163
4889036484665Charming one bedroom - newly renovated rowhouse8232441SabrinaBrooklynBedford-Stuyvesant40.67853-73.94995Private room7020NaNNaN29
4889136485057Affordable room in Bushwick/East Williamsburg6570630MarisolBrooklynBushwick40.70184-73.93317Private room4040NaNNaN236
4889236485431Sunny Studio at Historical Neighborhood23492952Ilgar & AyselManhattanHarlem40.81475-73.94867Entire home/apt115100NaNNaN127
488933648560943rd St. Time Square-cozy single bed30985759TazManhattanHell's Kitchen40.75751-73.99112Shared room5510NaNNaN62
4889436487245Trendy duplex in the very heart of Hell's Kitchen68119814ChristopheManhattanHell's Kitchen40.76404-73.98933Private room9070NaNNaN123